
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			KandinskyVaaImgaImgPipeline
     lowerCAmelCase_							   =			['''image_embeds''', '''negative_image_embeds''', '''image''']
     lowerCAmelCase_							   =			[
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
     ]
     lowerCAmelCase_							   =			[
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     lowerCAmelCase_							   =			False



     @property
     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       return 32



     @property
     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       return 32



     @property
     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       return self.time_input_dim



     @property
     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       return self.time_input_dim * 4



     @property
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       return 100



     @property
     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       torch.manual_seed(0 )

       __SCREAMING_SNAKE_CASE      :	Dict					=		{
           '''in_channels''': 4,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       __SCREAMING_SNAKE_CASE      :	Dict					=		UNetaDConditionModel(**_A )
       return model



     @property
     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }



     @property
     def  UpperCAmelCase__							(				self							:							str ):







       """simple docstring"""




       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	str					=		VQModel(**self.dummy_movq_kwargs )
       return model



     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.dummy_unet
       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.dummy_movq

       __SCREAMING_SNAKE_CASE      :	Dict					=		{
           '''num_train_timesteps''': 1000,
           '''beta_schedule''': '''linear''',
           '''beta_start''': 0.0_00_85,
           '''beta_end''': 0.0_12,
           '''clip_sample''': False,
           '''set_alpha_to_one''': False,
           '''steps_offset''': 0,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
       }

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		DDIMScheduler(**_A )

       __SCREAMING_SNAKE_CASE      :	Any					=		{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components



     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							List[str]     ,						_A							:							str=0 ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(_A ) ).to(_A )
       __SCREAMING_SNAKE_CASE      :	str					=		floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(seed + 1 ) ).to(
           _A )
       # create init_image
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		floats_tensor((1, 3, 64, 64)     ,						rng=random.Random(_A ) ).to(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		image.cpu().permute(0     ,						2     ,						3     ,						1 )[0]
       __SCREAMING_SNAKE_CASE      :	str					=		Image.fromarray(np.uinta(_A ) ).convert('''RGB''' ).resize((256, 256) )

       if str(_A ).startswith('''mps''' ):
         __SCREAMING_SNAKE_CASE      :	Tuple					=		torch.manual_seed(_A )
       else:
         __SCREAMING_SNAKE_CASE      :	int					=		torch.Generator(device=_A ).manual_seed(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		{
           '''image''': init_image,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 10,
           '''guidance_scale''': 7.0,
           '''strength''': 0.2,
           '''output_type''': '''np''',
       }
       return inputs




     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		'''cpu'''

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.get_dummy_components()

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.pipeline_class(**_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		pipe.to(_A )

       pipe.set_progress_bar_config(disable=_A )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		pipe(**self.get_dummy_inputs(_A ) )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		output.images

       __SCREAMING_SNAKE_CASE      :	str					=		pipe(
           **self.get_dummy_inputs(_A )     ,						return_dict=_A     ,						)[0]

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		image[0, -3:, -3:, -1]
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       __SCREAMING_SNAKE_CASE      :	Tuple					=		np.array(
           [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63] )
       assert (
           np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class     __UpperCamelCase (							unittest.TestCase       ):






     """simple docstring"""







     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
       __SCREAMING_SNAKE_CASE      :	Any					=		'''A red cartoon frog, 4k'''

       __SCREAMING_SNAKE_CASE      :	Any					=		KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''     ,						torch_dtype=torch.floataa )
       pipe_prior.to(_A )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		KandinskyVaaImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder'''     ,						torch_dtype=torch.floataa )
       __SCREAMING_SNAKE_CASE      :	int					=		pipeline.to(_A )

       pipeline.set_progress_bar_config(disable=_A )

       __SCREAMING_SNAKE_CASE      :	Any					=		torch.Generator(device='''cpu''' ).manual_seed(0 )
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		pipe_prior(
           _A     ,						generator=_A     ,						num_inference_steps=5     ,						negative_prompt=''''''     ,						).to_tuple()

       __SCREAMING_SNAKE_CASE      :	Dict					=		pipeline(
           image=_A     ,						image_embeds=_A     ,						negative_image_embeds=_A     ,						generator=_A     ,						num_inference_steps=100     ,						height=768     ,						width=768     ,						strength=0.2     ,						output_type='''np'''     ,						)

       __SCREAMING_SNAKE_CASE      :	int					=		output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(_A     ,						_A )

def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				len(lowercase				)
				__lowercase					     =				len(lowercase				)
				__lowercase					     =				[[False for _ in range(m + 1				)] for _ in range(n + 1				)]
				__lowercase					     =				True
				for i in range(lowercase				):
								for j in range(m + 1				):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				__lowercase					     =				True
																if a[i].islower():
																				__lowercase					     =				True
				return dp[n][m]


if __name__ == "__main__":
		import doctest

		doctest.testmod()